Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "201" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 29 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 29 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460012 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.621237 | 4.777953 | 2.428562 | 3.486710 | 2.240279 | 5.209103 | -2.139682 | -4.384735 | 0.5664 | 0.5670 | 0.3428 | nan | nan |
| 2460011 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.396438 | 5.157529 | 3.591553 | 4.827588 | 5.854797 | 11.043593 | -1.261623 | -3.163434 | 0.5891 | 0.5912 | 0.3413 | nan | nan |
| 2460010 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.901140 | 5.733540 | 3.433658 | 4.132067 | 3.509576 | 7.162827 | -1.882645 | -3.280906 | 0.6033 | 0.6077 | 0.3463 | nan | nan |
| 2460009 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.594928 | 5.178152 | 3.264611 | 4.099676 | 3.598071 | 6.162703 | -2.171200 | -3.266616 | 0.6048 | 0.6085 | 0.3513 | nan | nan |
| 2460008 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.133457 | 5.755238 | 3.815329 | 4.726761 | 3.068849 | 5.465718 | 1.838267 | 3.280525 | 0.6368 | 0.6411 | 0.3239 | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.940710 | 4.424696 | 2.830090 | 3.560749 | 1.697218 | 4.906320 | -1.495720 | -3.023945 | 0.6102 | 0.6145 | 0.3387 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 0.08% | 0.08% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5355 | 0.5433 | 0.3457 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.707727 | 4.242213 | 2.529926 | 3.382516 | 1.817641 | 6.577728 | -0.844487 | -2.785165 | 0.5940 | 0.5973 | 0.3742 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.954907 | 4.536954 | 2.899105 | 3.616840 | 2.458978 | 6.406191 | -1.698724 | -4.676962 | 0.6037 | 0.6076 | 0.3793 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.287877 | 5.135849 | 3.067233 | 4.120904 | 3.110074 | 6.420312 | -1.353022 | -2.489843 | 0.6162 | 0.6159 | 0.3864 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.311230 | 5.009403 | 3.173725 | 4.140831 | 3.175233 | 6.148312 | -0.950536 | -2.448827 | 0.6056 | 0.6108 | 0.3764 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.926190 | 4.785822 | 2.957404 | 3.703590 | 3.126011 | 6.315637 | -0.845341 | -2.284493 | 0.6003 | 0.6049 | 0.3735 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.519742 | 6.827676 | 3.203883 | 4.030161 | 3.826019 | 6.875593 | -0.583220 | -0.956600 | 0.5779 | 0.5972 | 0.3888 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.653889 | 5.866307 | 3.174328 | 4.045233 | 2.854528 | 7.087923 | -0.480278 | -2.357296 | 0.6108 | 0.6062 | 0.3810 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.029499 | 5.068085 | 3.160121 | 4.173914 | 2.862936 | 7.298519 | -0.607198 | -2.826868 | 0.6088 | 0.6077 | 0.3782 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.917164 | 5.053185 | 3.018565 | 3.697393 | 2.519381 | 5.893914 | -0.510474 | -2.223969 | 0.5993 | 0.6040 | 0.3824 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.638339 | 6.110808 | 3.303268 | 4.319653 | 3.437670 | 8.598525 | -0.429820 | -2.164978 | 0.6027 | 0.6067 | 0.3749 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.617830 | 4.629191 | 2.924737 | 3.713049 | 2.056438 | 5.306905 | -1.273252 | -3.192181 | 0.6120 | 0.6127 | 0.3685 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.793734 | 5.977657 | 3.259618 | 4.250043 | 3.309878 | 7.606508 | 2.010834 | 5.603441 | 0.6233 | 0.6285 | 0.3450 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.287389 | 5.523690 | 2.868136 | 3.687748 | 2.428518 | 5.588369 | -1.265031 | -3.839534 | 0.6101 | 0.6112 | 0.3766 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.042391 | 4.955724 | 2.925919 | 3.745971 | 5.018492 | 8.572818 | -0.592324 | -0.830624 | 0.6233 | 0.6260 | 0.3562 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.947714 | 4.855455 | 3.038306 | 4.052985 | 2.768377 | 7.297171 | 0.141504 | 2.496213 | 0.6376 | 0.6420 | 0.3295 | nan | nan |
| 2459982 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.467209 | 2.032797 | 2.095937 | 2.925248 | 0.898485 | 2.845516 | 0.571532 | 1.985096 | 0.6771 | 0.6626 | 0.3061 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.555681 | 4.570327 | 3.496489 | 4.707816 | 3.987982 | 8.153115 | -0.728467 | -2.566516 | 0.6178 | 0.6151 | 0.3764 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.345789 | 4.053306 | 2.785175 | 3.664624 | 2.653937 | 6.758757 | 2.159105 | 3.311671 | 0.6493 | 0.6410 | 0.3198 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.523127 | 4.506531 | 2.842590 | 3.675149 | 2.418527 | 6.339035 | -0.713483 | -2.463139 | 0.6100 | 0.6110 | 0.3764 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.565365 | 4.738017 | 3.163657 | 4.152007 | 2.761528 | 7.061661 | -0.329115 | -2.831855 | 0.6105 | 0.6093 | 0.3841 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.033990 | 4.863963 | 2.759447 | 3.538465 | 2.838614 | 7.294329 | -1.643114 | -3.226673 | 0.5740 | 0.5712 | 0.3427 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.680465 | 4.689289 | 3.055410 | 4.081949 | 2.825920 | 6.866822 | -0.268994 | -1.756343 | 0.6161 | 0.6150 | 0.3769 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 5.209103 | 2.621237 | 4.777953 | 2.428562 | 3.486710 | 2.240279 | 5.209103 | -2.139682 | -4.384735 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 11.043593 | 3.396438 | 5.157529 | 3.591553 | 4.827588 | 5.854797 | 11.043593 | -1.261623 | -3.163434 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 7.162827 | 3.901140 | 5.733540 | 3.433658 | 4.132067 | 3.509576 | 7.162827 | -1.882645 | -3.280906 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.162703 | 3.594928 | 5.178152 | 3.264611 | 4.099676 | 3.598071 | 6.162703 | -2.171200 | -3.266616 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Shape | 5.755238 | 5.755238 | 4.133457 | 4.726761 | 3.815329 | 5.465718 | 3.068849 | 3.280525 | 1.838267 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 4.906320 | 2.940710 | 4.424696 | 2.830090 | 3.560749 | 1.697218 | 4.906320 | -1.495720 | -3.023945 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.577728 | 2.707727 | 4.242213 | 2.529926 | 3.382516 | 1.817641 | 6.577728 | -0.844487 | -2.785165 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.406191 | 2.954907 | 4.536954 | 2.899105 | 3.616840 | 2.458978 | 6.406191 | -1.698724 | -4.676962 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.420312 | 3.287877 | 5.135849 | 3.067233 | 4.120904 | 3.110074 | 6.420312 | -1.353022 | -2.489843 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.148312 | 3.311230 | 5.009403 | 3.173725 | 4.140831 | 3.175233 | 6.148312 | -0.950536 | -2.448827 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.315637 | 2.926190 | 4.785822 | 2.957404 | 3.703590 | 3.126011 | 6.315637 | -0.845341 | -2.284493 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.875593 | 3.519742 | 6.827676 | 3.203883 | 4.030161 | 3.826019 | 6.875593 | -0.583220 | -0.956600 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 7.087923 | 3.653889 | 5.866307 | 3.174328 | 4.045233 | 2.854528 | 7.087923 | -0.480278 | -2.357296 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 7.298519 | 5.068085 | 3.029499 | 4.173914 | 3.160121 | 7.298519 | 2.862936 | -2.826868 | -0.607198 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 5.893914 | 5.053185 | 2.917164 | 3.697393 | 3.018565 | 5.893914 | 2.519381 | -2.223969 | -0.510474 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 8.598525 | 6.110808 | 3.638339 | 4.319653 | 3.303268 | 8.598525 | 3.437670 | -2.164978 | -0.429820 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 5.306905 | 2.617830 | 4.629191 | 2.924737 | 3.713049 | 2.056438 | 5.306905 | -1.273252 | -3.192181 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 7.606508 | 5.977657 | 3.793734 | 4.250043 | 3.259618 | 7.606508 | 3.309878 | 5.603441 | 2.010834 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 5.588369 | 5.523690 | 3.287389 | 3.687748 | 2.868136 | 5.588369 | 2.428518 | -3.839534 | -1.265031 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 8.572818 | 3.042391 | 4.955724 | 2.925919 | 3.745971 | 5.018492 | 8.572818 | -0.592324 | -0.830624 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 7.297171 | 2.947714 | 4.855455 | 3.038306 | 4.052985 | 2.768377 | 7.297171 | 0.141504 | 2.496213 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Power | 2.925248 | 0.467209 | 2.032797 | 2.095937 | 2.925248 | 0.898485 | 2.845516 | 0.571532 | 1.985096 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 8.153115 | 4.570327 | 2.555681 | 4.707816 | 3.496489 | 8.153115 | 3.987982 | -2.566516 | -0.728467 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.758757 | 4.053306 | 2.345789 | 3.664624 | 2.785175 | 6.758757 | 2.653937 | 3.311671 | 2.159105 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.339035 | 2.523127 | 4.506531 | 2.842590 | 3.675149 | 2.418527 | 6.339035 | -0.713483 | -2.463139 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 7.061661 | 4.738017 | 2.565365 | 4.152007 | 3.163657 | 7.061661 | 2.761528 | -2.831855 | -0.329115 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 7.294329 | 3.033990 | 4.863963 | 2.759447 | 3.538465 | 2.838614 | 7.294329 | -1.643114 | -3.226673 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201 | N18 | RF_maintenance | nn Temporal Variability | 6.866822 | 4.689289 | 2.680465 | 4.081949 | 3.055410 | 6.866822 | 2.825920 | -1.756343 | -0.268994 |